arXiv AI

Reconstruction Alignment Improves Unified Multimodal Models

arXiv:2509. 07295v4 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture.

arXiv AI

Jun 3

Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models

arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.

By Xinpeng Dong, Min Zhang, Kairong Han, Xu Tan, Fei Wu, Kun Kuang

llms computer-vision multimodal benchmarks safety

Hugging Face Trending Papers

Jun 9

One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QA

External memory effectively grounds large language models (LLMs) and vision-language models (VLMs)-based question answering (QA) in relevant multimodal evidence. However, existing memory paradigms represent each memory item in raw text and image forms, so retrieval-based systems must pass the retrieved text or images to the generation LLMs/VLMs, resulting in high token consumption and storage pressure, making it unaffordable for resource-constrained applications.

llms rag nlp efficiency multimodal benchmarks

arXiv AI

Jul 16

Inverse-LLaVA: Rethinking Multimodal Alignment via Text-to-Vision Mapping

arXiv:2508. 12466v2 Announce Type: replace-cross Abstract: Traditional multimodal learning approaches rely on alignment pre-training to bridge vision and language modalities, typically by projecting visual features into discrete text token spaces using large-scale image--text data.

By Xuhui Zhan, Tyler Derr

llms rag multimodal benchmarks safety

arXiv AI

Jun 16

Improved Baselines with Representation Autoencoders

arXiv:2605. 18324v2 Announce Type: replace-cross Abstract: Representation Autoencoders (RAE) replace traditional VAE with pretrained vision encoders.

By Jaskirat Singh, Boyang Zheng, Zongze Wu, Richard Zhang, Eli Shechtman, Saining Xie

diffusion fine-tuning benchmarks safety

arXiv AI

Jun 8

Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models

arXiv:2602. 07026v3 Announce Type: replace-cross Abstract: Despite the success of multimodal contrastive learning in aligning visual and linguistic representations, a persistent geometric anomaly, the Modality Gap, remains: embeddings of distinct modalities expressing identical semantics occupy systematically offset regions.

By Xiaomin Yu, Yi Xin, Yuhui Zhang, Wenjie Zhang, Chonghan Liu, Hanzhen Zhao, Chen Liu, Xiaoxing Hu, Ziyue Qiao, Hao Tang, Xiaobin Hu, Chengwei Qin, Hui Xiong, Yu Qiao, Shuicheng Yan

llms rag fine-tuning multimodal safety

arXiv AI

Jun 17

Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Model

arXiv:2606. 17950v1 Announce Type: cross Abstract: Visual information helps resolve ambiguity in coreference resolution, leading to notable performance gains.

By Jinghan Wu, Jing Li, Ivor W. Tsang, Xuetao Zhang

multimodal benchmarks safety

arXiv AI

Jun 10

One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QA

arXiv:2606. 10572v1 Announce Type: new Abstract: External memory effectively grounds large language models (LLMs) and vision-language models (VLMs)-based question answering (QA) in relevant multimodal evidence.

By Zhi Zheng, Ziqiao Meng, Hao Luan, Wei Liu, Wee Sun Lee

llms rag nlp efficiency multimodal benchmarks

arXiv Machine Learning

Jul 9

Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering

arXiv:2607. 07179v1 Announce Type: cross Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents.

By Miguel Lopez-Duran, Elena Marrero, Julian Fierrez, Marta Robledo-Moreno, Ruben Vera-Rodriguez, Daniel DeAlcala, Aythami Morales, Ruben Tolosana, Oscar Delgado, Alvaro Ortigosa, Javier Ortega-Garcia

llms nlp fine-tuning multimodal

arXiv AI

Jun 30

Multimodal Representation Alignment for Cross-modal Information Retrieval

arXiv:2506. 08774v2 Announce Type: replace-cross Abstract: Different machine learning models can represent the same underlying concept in different ways.

By Fan Xu, Luis A. Leiva

llms rag multimodal benchmarks safety

arXiv AI

Jul 16

Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation

arXiv:2607. 13125v1 Announce Type: cross Abstract: We introduce Boogu-Image-0.

By Guoxuan Chen, Chufeng Xiao, Haoran Yang, Siyue Xie, Binxiao Huang, Ming Zhang, Cheuk Him Chau, Xinyu Fu, Yingzhao Lian, Tom S. Y. Li, Jintao Lin, Bowen Dong, Zian Qian, Yuhao Liu, Yuxuan Hu, Weikang Shi, Bin Zou, Bowen Zheng, Haoxuan Che, Chang Chen, Yuyang He, Heyang Sun, Tianyu Huang, Chong Hou Choi, Cheng Gong, Han Shi, Haoli Bai, Xihui Liu, Hongsheng Li, Qifeng Chen, Chao Huang, Rui Liu, Chenyang Lei

llms agents diffusion multimodal benchmarks data-engineering

arXiv AI

Jul 10

Reinforcing the Generation Order of Multimodal Masked Diffusion Models

arXiv:2607. 08056v1 Announce Type: cross Abstract: Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks.

By Yidong Ouyang, Zhe Wang, Sourav Bhabesh, Dmitriy Bespalov

llms diffusion multimodal benchmarks safety

arXiv AI

Jul 3

SEPS: Semantic-enhanced Patch Slimming Framework for fine-grained cross-modal alignment

arXiv:2511. 01390v2 Announce Type: replace-cross Abstract: Fine-grained cross-modal alignment aims to establish precise local correspondences between vision and language, forming a cornerstone for visual question answering and related multimodal applications.

By Xinyu Mao, Junsi Li, Haoji Zhang, Yu Liang, Ming Sun

llms diffusion nlp multimodal safety

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